OpenRaiser/Intern-Atlas huggingface dataset:
Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles
Venue: arXiv cs.HC 2023
Year: 2023

Interaction-aware Autonomous Driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the autonomous vehicle to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed in this work. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modelling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modelling, encompassing cognitive methods, machine learning approaches, and game-theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration.
An Ontological Model of User Preferences
Venue: arXiv cs.RO 2023
Year: 2023

The notion of preferences plays an important role in many disciplines including service robotics which is concerned with scenarios in which robots interact with humans. These interactions can be favored by robots taking human preferences into account. This raises the issue of how preferences should be represented to support such preference-aware decision making. Several formal accounts for a notion of preferences exist. However, these approaches fall short on defining the nature and structure of the options that a robot has in a given situation. In this work, we thus investigate a formal model of preferences where options are non-atomic entities that are defined by the complex situations they bring about.
Customize StyleGAN with One Hand Sketch
Venue: arXiv cs.CV 2023
Year: 2023

Generating images from human sketches typically requires dedicated networks trained from scratch. In contrast, the emergence of the pre-trained Vision-Language models (e.g., CLIP) has propelled generative applications based on controlling the output imagery of existing StyleGAN models with text inputs or reference images. Parallelly, our work proposes a framework to control StyleGAN imagery with a single user sketch. In particular, we learn a conditional distribution in the latent space of a pre-trained StyleGAN model via energy-based learning and propose two novel energy functions leveraging CLIP for cross-domain semantic supervision. Once trained, our model can generate multi-modal images semantically aligned with the input sketch. Quantitative evaluations on synthesized datasets have shown that our approach improves significantly from previous methods in the one-shot regime. The superiority of our method is further underscored when experimenting with a wide range of human sketches of diverse styles and poses. Surprisingly, our models outperform the previous baseline regarding both the range of sketch inputs and image qualities despite operating with a stricter setting: with no extra training data and single sketch input.
EtiCor: Corpus for Analyzing LLMs for Etiquettes
Venue: arXiv cs.CL 2023
Year: 2023

Etiquettes are an essential ingredient of day-to-day interactions among people. Moreover, etiquettes are region-specific, and etiquettes in one region might contradict those in other regions. In this paper, we propose EtiCor, an Etiquettes Corpus, having texts about social norms from five different regions across the globe. The corpus provides a test bed for evaluating LLMs for knowledge and understanding of region-specific etiquettes. Additionally, we propose the task of Etiquette Sensitivity. We experiment with state-of-the-art LLMs (Delphi, Falcon40B, and GPT-3.5). Initial results indicate that LLMs, mostly fail to understand etiquettes from regions from non-Western world.
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data
Venue: arXiv cs.CV 2023
Year: 2023

Training fingerprint recognition models using synthetic data has recently gained increased attention in the biometric community as it alleviates the dependency on sensitive personal data. Existing approaches for fingerprint generation are limited in their ability to generate diverse impressions of the same finger, a key property for providing effective data for training recognition models. To address this gap, we present FPGAN-Control, an identity preserving image generation framework which enables control over the fingerprint's image appearance (e.g., fingerprint type, acquisition device, pressure level) of generated fingerprints. We introduce a novel appearance loss that encourages disentanglement between the fingerprint's identity and appearance properties. In our experiments, we used the publicly available NIST SD302 (N2N) dataset for training the FPGAN-Control model. We demonstrate the merits of FPGAN-Control, both quantitatively and qualitatively, in terms of identity preservation level, degree of appearance control, and low synthetic-to-real domain gap. Finally, training recognition models using only synthetic datasets generated by FPGAN-Control lead to recognition accuracies that are on par or even surpass models trained using real data. To the best of our knowledge, this is the first work to demonstrate this.
SALMA: Arabic Sense-Annotated Corpus and WSD Benchmarks
Venue: arXiv cs.CL 2023
Year: 2023

SALMA, the first Arabic sense-annotated corpus, consists of ~34K tokens, which are all sense-annotated. The corpus is annotated using two different sense inventories simultaneously (Modern and Ghani). SALMA novelty lies in how tokens and senses are associated. Instead of linking a token to only one intended sense, SALMA links a token to multiple senses and provides a score to each sense. A smart web-based annotation tool was developed to support scoring multiple senses against a given word. In addition to sense annotations, we also annotated the corpus using six types of named entities. The quality of our annotations was assessed using various metrics (Kappa, Linear Weighted Kappa, Quadratic Weighted Kappa, Mean Average Error, and Root Mean Square Error), which show very high inter-annotator agreement. To establish a Word Sense Disambiguation baseline using our SALMA corpus, we developed an end-to-end Word Sense Disambiguation system using Target Sense Verification. We used this system to evaluate three Target Sense Verification models available in the literature. Our best model achieved an accuracy with 84.2% using Modern and 78.7% using Ghani. The full corpus and the annotation tool are open-source and publicly available at https://sina.birzeit.edu/salma/.
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression
Venue: arXiv cs.LG 2023
Year: 2023

We consider the problem of finding second-order stationary points of heterogeneous federated learning (FL). Previous works in FL mostly focus on first-order convergence guarantees, which do not rule out the scenario of unstable saddle points. Meanwhile, it is a key bottleneck of FL to achieve communication efficiency without compensating the learning accuracy, especially when local data are highly heterogeneous across different clients. Given this, we propose a novel algorithm Power-EF that only communicates compressed information via a novel error-feedback scheme. To our knowledge, Power-EF is the first distributed and compressed SGD algorithm that provably escapes saddle points in heterogeneous FL without any data homogeneity assumptions. In particular, Power-EF improves to second-order stationary points after visiting first-order (possibly saddle) points, using additional gradient queries and communication rounds only of almost the same order required by first-order convergence, and the convergence rate exhibits a linear speedup in terms of the number of workers. Our theory improves/recovers previous results, while extending to much more tolerant settings on the local data. Numerical experiments are provided to complement the theory.
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach
Venue: arXiv cs.LG 2023
Year: 2023

Federated learning (FL) offers a decentralized training approach for machine learning models, prioritizing data privacy. However, the inherent heterogeneity in FL networks, arising from variations in data distribution, size, and device capabilities, poses challenges in user federation. Recognizing this, Personalized Federated Learning (PFL) emphasizes tailoring learning processes to individual data profiles. In this paper, we address the complexity of clustering users in PFL, especially in dynamic networks, by introducing a dynamic Upper Confidence Bound (dUCB) algorithm inspired by the multi-armed bandit (MAB) approach. The dUCB algorithm ensures that new users can effectively find the best cluster for their data distribution by balancing exploration and exploitation. The performance of our algorithm is evaluated in various cases, showing its effectiveness in handling dynamic federated learning scenarios.
Web3 Meets AI Marketplace: Exploring Opportunities, Analyzing Challenges, and Suggesting Solutions
Venue: arXiv cs.AI 2023
Year: 2023

Web3 and AI have been among the most discussed fields over the recent years, with substantial hype surrounding each field's potential to transform the world as we know it. However, as the hype settles, it's evident that neither AI nor Web3 can address all challenges independently. Consequently, the intersection of AI and Web3 is gaining increased attention, emerging as a new field with the potential to address the limitations of each. In this article, we will focus on the integration of web3 and the AI marketplace, where AI services and products can be provided in a decentralized manner (DeAI). A comprehensive review is provided by summarizing the opportunities and challenges on this topic. Additionally, we offer analyses and solutions to address these challenges. We've developed a framework that lets users pay with any kind of cryptocurrency to get AI services. Additionally, they can also enjoy AI services for free on our platform by simply locking up their assets temporarily in the protocol. This unique approach is a first in the industry. Before this, offering free AI services in the web3 community wasn't possible. Our solution opens up exciting opportunities for the AI marketplace in the web3 space to grow and be widely adopted.
Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender
Venue: arXiv cs.CL 2023
Year: 2023

In this paper, we investigate the impact of objects on gender bias in image captioning systems. Our results show that only gender-specific objects have a strong gender bias (e.g., women-lipstick). In addition, we propose a visual semantic-based gender score that measures the degree of bias and can be used as a plug-in for any image captioning system. Our experiments demonstrate the utility of the gender score, since we observe that our score can measure the bias relation between a caption and its related gender; therefore, our score can be used as an additional metric to the existing Object Gender Co-Occ approach. Code and data are publicly available at \url{https://github.com/ahmedssabir/GenderScore}.
The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation
Venue: arXiv cs.LG 2023
Year: 2023

Floods can cause horrific harm to life and property. However, they can be mitigated or even avoided by the effective use of hydraulic structures such as dams, gates, and pumps. By pre-releasing water via these structures in advance of extreme weather events, water levels are sufficiently lowered to prevent floods. In this work, we propose FIDLAR, a Forecast Informed Deep Learning Architecture, achieving flood management in watersheds with hydraulic structures in an optimal manner by balancing out flood mitigation and unnecessary wastage of water via pre-releases. We perform experiments with FIDLAR using data from the South Florida Water Management District, which manages a coastal area that is highly prone to frequent storms and floods. Results show that FIDLAR performs better than the current state-of-the-art with several orders of magnitude speedup and with provably better pre-release schedules. The dramatic speedups make it possible for FIDLAR to be used for real-time flood management. The main contribution of this paper is the effective use of tools for model explainability, allowing us to understand the contribution of the various environmental factors towards its decisions.
Rare Event Probability Learning by Normalizing Flows
Venue: arXiv cs.LG 2023
Year: 2023

A rare event is defined by a low probability of occurrence. Accurate estimation of such small probabilities is of utmost importance across diverse domains. Conventional Monte Carlo methods are inefficient, demanding an exorbitant number of samples to achieve reliable estimates. Inspired by the exact sampling capabilities of normalizing flows, we revisit this challenge and propose normalizing flow assisted importance sampling, termed NOFIS. NOFIS first learns a sequence of proposal distributions associated with predefined nested subset events by minimizing KL divergence losses. Next, it estimates the rare event probability by utilizing importance sampling in conjunction with the last proposal. The efficacy of our NOFIS method is substantiated through comprehensive qualitative visualizations, affirming the optimality of the learned proposal distribution, as well as a series of quantitative experiments encompassing $10$ distinct test cases, which highlight NOFIS's superiority over baseline approaches.
Identification of the Most Significant Parameter for Optimizing the Performance of RPL Routing Protocol in IoT Using Taguchi Design of Experiments
Venue: arXiv cs.NI 2023
Year: 2023

Internet of Things (IoT) consists of a wide variety of devices with limited power sources. Due to the adhered reason, energy consumption is considered as one of the major challenges in the IoT environment. In this research article, an attempt is made to optimize the existing Routing Protocol (RPL) towards a green technology. It focuses on finding the most significant parameter in the RPL using Taguchi Design of Experiments. It emphasizes the effects of five input factors, such as Network Size, Mobility Speed, DIO_DOUBLING, DIO_MIN_INTERVAL, and Redundancy Constant on only one output parameter Power Consumption. The findings show that DIO_MIN_INTERVAL is the leading factor that has a significant effect on the power consumption in RPL. After determining the most significant factor that affects the power consumption, measures can be taken to optimize the performance of RPL by applying some optimization techniques. COOJA simulator is used to carry out the simulations required for this research article.
Immersive 3D Simulator for Drone-as-a-Service
Venue: arXiv cs.RO 2023
Year: 2023

We propose a 3D simulator tailored for the Drone-as-a-Service framework. The simulator enables employing dynamic algorithms for addressing realistic delivery scenarios. We present the simulator's architectural design and its use of an energy consumption model for drone deliveries. We introduce two primary operational modes within the simulator: the edit mode and the runtime mode. Beyond its simulation capabilities, our simulator serves as a valuable data collection resource, facilitating the creation of datasets through simulated scenarios. Our simulator empowers researchers by providing an intuitive platform to visualize and interact with delivery environments. Moreover, it enables rigorous algorithm testing in a safe simulation setting, thus obviating the need for real-world drone deployments. Demo: https://youtu.be/HOLfo1JiFJ0
Leveraging generative artificial intelligence to simulate student learning behavior
Venue: arXiv cs.AI 2023
Year: 2023

Student simulation presents a transformative approach to enhance learning outcomes, advance educational research, and ultimately shape the future of effective pedagogy. We explore the feasibility of using large language models (LLMs), a remarkable achievement in AI, to simulate student learning behaviors. Unlike conventional machine learning based prediction, we leverage LLMs to instantiate virtual students with specific demographics and uncover intricate correlations among learning experiences, course materials, understanding levels, and engagement. Our objective is not merely to predict learning outcomes but to replicate learning behaviors and patterns of real students. We validate this hypothesis through three experiments. The first experiment, based on a dataset of N = 145, simulates student learning outcomes from demographic data, revealing parallels with actual students concerning various demographic factors. The second experiment (N = 4524) results in increasingly realistic simulated behaviors with more assessment history for virtual students modelling. The third experiment (N = 27), incorporating prior knowledge and course interactions, indicates a strong link between virtual students' learning behaviors and fine-grained mappings from test questions, course materials, engagement and understanding levels. Collectively, these findings deepen our understanding of LLMs and demonstrate its viability for student simulation, empowering more adaptable curricula design to enhance inclusivity and educational effectiveness.
Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering
Venue: arXiv cs.CL 2023
Year: 2023

Answering time-sensitive questions from long documents requires temporal reasoning over the times in questions and documents. An important open question is whether large language models can perform such reasoning solely using a provided text document, or whether they can benefit from additional temporal information extracted using other systems. We address this research question by applying existing temporal information extraction systems to construct temporal graphs of events, times, and temporal relations in questions and documents. We then investigate different approaches for fusing these graphs into Transformer models. Experimental results show that our proposed approach for fusing temporal graphs into input text substantially enhances the temporal reasoning capabilities of Transformer models with or without fine-tuning. Additionally, our proposed method outperforms various graph convolution-based approaches and establishes a new state-of-the-art performance on SituatedQA and three splits of TimeQA.
Extracting user needs with Chat-GPT for dialogue recommendation
Venue: arXiv cs.CY 2023
Year: 2023

Large-scale language models (LLMs), such as ChatGPT, are becoming increasingly sophisticated and exhibit human-like capabilities, playing an essential role in assisting humans in a variety of everyday tasks. An important application of AI is interactive recommendation systems that respond to human inquiries and make recommendations tailored to the user. In most conventional interactive recommendation systems, the language model is used only as a dialogue model, and there is a separate recommendation system. This is due to the fact that the language model used as a dialogue system does not have the capability to serve as a recommendation system. Therefore, we will realize the construction of a dialogue system with recommendation capability by using OpenAI's Chat-GPT, which has a very high inference capability as a dialogue system and the ability to generate high-quality sentences, and verify the effectiveness of the system.
Skywork: A More Open Bilingual Foundation Model
Venue: arXiv cs.CL 2023
Year: 2023

In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both English and Chinese texts. This bilingual foundation model is the most extensively trained and openly published LLMs of comparable size to date. We introduce a two-stage training methodology using a segmented corpus, targeting general purpose training and then domain-specific enhancement training, respectively. We show that our model not only excels on popular benchmarks, but also achieves \emph{state of the art} performance in Chinese language modeling on diverse domains. Furthermore, we propose a novel leakage detection method, demonstrating that test data contamination is a pressing issue warranting further investigation by the LLM community. To spur future research, we release Skywork-13B along with checkpoints obtained during intermediate stages of the training process. We are also releasing part of our SkyPile corpus, a collection of over 150 billion tokens of web text, which is the largest high quality open Chinese pre-training corpus to date. We hope Skywork-13B and our open corpus will serve as a valuable open-source resource to democratize access to high-quality LLMs.
Few-shot Hybrid Domain Adaptation of Image Generators
Venue: arXiv cs.CV 2023
Year: 2023

Can a pre-trained generator be adapted to the hybrid of multiple target domains and generate images with integrated attributes of them? In this work, we introduce a new task -- Few-shot Hybrid Domain Adaptation (HDA). Given a source generator and several target domains, HDA aims to acquire an adapted generator that preserves the integrated attributes of all target domains, without overriding the source domain's characteristics. Compared with Domain Adaptation (DA), HDA offers greater flexibility and versatility to adapt generators to more composite and expansive domains. Simultaneously, HDA also presents more challenges than DA as we have access only to images from individual target domains and lack authentic images from the hybrid domain. To address this issue, we introduce a discriminator-free framework that directly encodes different domains' images into well-separable subspaces. To achieve HDA, we propose a novel directional subspace loss comprised of a distance loss and a direction loss. Concretely, the distance loss blends the attributes of all target domains by reducing the distances from generated images to all target subspaces. The direction loss preserves the characteristics from the source domain by guiding the adaptation along the perpendicular to subspaces. Experiments show that our method can obtain numerous domain-specific attributes in a single adapted generator, which surpasses the baseline methods in semantic similarity, image fidelity, and cross-domain consistency.
Mean BERTs make erratic language teachers: the effectiveness of latent bootstrapping in low-resource settings
Venue: arXiv cs.CL 2023
Year: 2023

This paper explores the use of latent bootstrapping, an alternative self-supervision technique, for pretraining language models. Unlike the typical practice of using self-supervision on discrete subwords, latent bootstrapping leverages contextualized embeddings for a richer supervision signal. We conduct experiments to assess how effective this approach is for acquiring linguistic knowledge from limited resources. Specifically, our experiments are based on the BabyLM shared task, which includes pretraining on two small curated corpora and an evaluation on four linguistic benchmarks.
FoodFresh: Multi-Chain Design for an Inter-Institutional Food Supply Chain Network
Venue: arXiv cs.DC 2023
Year: 2023

We consider the problem of supply chain data visibility in a blockchain-enabled supply chain network. Existing methods typically record transactions happening in a supply chain on a single blockchain and are limited in their ability to deal with different levels of data visibility. To address this limitation, we present FoodFresh -- a multi-chain consortium where organizations store immutable data on their blockchains. A decentralized hub coordinates the cross-chain exchange of digital assets among the heterogeneous blockchains. Mechanisms for enabling blockchain interoperability help to preserve the benefits of independent sovereign blockchains while allowing for data sharing across blockchain boundaries.